title: Assessment of protein set coherence using functional annotations.
abstract: BACKGROUND
  Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information (e.g. significantly enriched annotations, pairwise functional similarities), they do not specifically measure the degree of homogeneity of a protein set.
  
  
  RESULTS
  In this work we present a method that scores the degree of functional homogeneity, or coherence, of a set of proteins on the basis of the global similarity of their functional annotations. The method uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set. As such, it can be used as a first step in the validation of sets expected to be homogeneous prior to further functional interpretation.
  
  
  CONCLUSIONS
  We evaluate our method by analysing known biologically relevant sets as well as random ones. The known relevant sets comprise macromolecular complexes, cellular components and pathways described for Saccharomyces cerevisiae, which are mostly significantly coherent. Finally, we illustrate the usefulness of our approach for validating 'functional modules' obtained from computational analysis of protein-protein interaction networks. Matlab code and supplementary data are available at http://www.cnb.csic.es/~monica/coherence/
authors.affiliation: Centro Nacional de Biotecnología-CSIC, Madrid, Spain. monica.chagoyen@cnb.csic.es
authors.forename: Monica
authors.initials: M
authors.lastname: Chagoyen
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carazo
authors.forename: Alberto
authors.initials: A
authors.lastname: Pascual-Montano
mesh_terms: D002176:Candida albicans / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005656:Fungal Proteins / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000737:chemistry / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*
chemical_list: D005656:Fungal Proteins; D029701:Saccharomyces cerevisiae Proteins